Biology subjects

Siria, D. J.

Publications and source records attributed to Siria, D. J..

4 recordsLinked to original sources

bioRxiv2026

How reliable is the use of ovarian dilatations for mosquito age-grading? A blinded multi-rater validation of the Polovodova method in Anopheles mosquitoes.

BackgroundAccurate estimation of mosquito age is critical for malaria surveillance, as only older female Anopheles mosquitoes can transmit the parasite. Traditional age-grading methods, include the Polovodova technique in which dilatations on the ovarian pedicel to tally completed gonotrophic cycles (GC) are counted. Despite their widespread use, these techniques remain poorly validated under realistic operational conditions, with limited evidence on their precision and accuracy across different parity rates, GC, and species. In this study, the accuracy and precision of the Polovodova method was evaluated across multiple age-graders and species under both laboratory and field conditions. MethodsA blinded validation study was conducted using laboratory-reared Anopheles arabiensis and Anopheles funestus (N > 3,600) spanning four gonotrophic cycles, and field-collected mosquitoes from Ulanga district, Tanzania (N = 600). Three blinded researchers performed dissections and readings, while a fourth unblinded researcher served as the reference reader (R0) to obtain ground truth (for parity) or assumed truth (for GC) for laboratory mosquitoes. Accuracy and precision for parity and gonotrophic cycle classification were assessed using pairwise inter-rater comparisons, percent agreement, Fleiss kappa and Cohens kappa O_SCPCAP(C_SCPCAPO_SCPLOWKC_SCPLOWO_SCPCAP)C_SCPCAP statistics. For field mosquitoes, which had no age-referenced group, only precision was assessed. FindingsThe Polovodova method accurately classified parity in both species, correctly identifying 98-100% of nulliparous and 94-96% of parous mosquitoes compared with the reference dataset. Inter-rater reliability for parity was almost perfect in An. arabiensis and An. funestus (k = 0.81-0.83), with 82-96% pairwise agreement. GC classification was highly accurate for early stages (GC0-GC2: 85-100%) but declined at later stages GC4(34-46%), reflecting systematic underestimation of mosquito age. Overall GC reliability was moderate (k = 0.56-0.59). Field classifications showed almost perfect agreement (k = 0.87-0.88), supporting operational applicability. ConclusionsThe Polovodova method provides robust estimates of parity and early gonotrophic-cycle status in Anopheles mosquitoes, but late-cycle classifications are more prone to reader disagreement and systematic underestimation. Operational use should therefore rely on trained multiple-reader workflows, including adjudication of discordant or late-cycle specimens, rather than single-reader classification. These findings provide empirical benchmarks for quality-assured Polovodova age-grading in malaria vector surveillance and intervention evaluation.

ecology↗

bioRxiv2025

Delayed copulation and mating in the malaria vector Anopheles funestus compared to Anopheles arabiensis

Mating is a vital behavior for mosquito reproduction, yet it remains poorly understood under captive conditions. We examined the copulation dynamics of two key malaria vectors, Anopheles funestus, and Anopheles arabiensis, in controlled laboratory settings in Tanzania. We observed how variations in mosquito age and artificial lighting influence mating success for these two mosquito species within cages under controlled conditions. We conducted observations in 24-hour cycles, monitoring copulation events and insemination in females. We used generalized linear mixed models (GLMMs) for statistical analyses to assess how environmental conditions influence mating behavior. We found that Anopheles arabiensis exhibited rapid copulation, with 32.4% of individuals mating by Day 3 post-emergence, while An. funestus showed delayed activity, reaching a similar mating rate by Day 8. The introduction of artificial red light significantly accelerated copulation in An. funestus but did not affect An. arabiensis. Dissection confirmed successful sperm transfer and mating plug delivery in over 92% of copulating pairs for both species. Mating occurred primarily at night, with distinct peaks at 22:00 for An. arabiensis and 23:00 for An. funestus. In conclusion, our findings reveal species-specific differences in reproductive behavior, which could improve the colonization of An. funestus, a species historically challenging to rear in captivity. These insights may also inform the development of new vector control technologies, such as sterile insect techniques and genetic-based approaches, that exploit mosquito mating behaviors.

ecology↗

bioRxiv2022

Using transfer learning and dimensionality reduction techniques to improve generalisability of machine-learning predictions of mosquito ages from mid-infrared spectra

Accurate prediction of mosquito population age structures can improve the evaluation of mosquito-targeted interventions since old mosquitoes are more likely to transmit malaria than young ones. Mid-infrared spectroscopy (MIRS) reveals age-associated variation in the biochemical composition of the mosquito cuticle, which can then be used to train machine learning (ML) models to predict mosquito ages. However, these MIRS-ML models are not always generalisable across different mosquito populations. Here, we investigated whether dimensionality reduction applied to the MIRS input data and transfer learning could improve the generalisability of MIRS-ML predictions for mosquito ages. We reared adults of the malaria vector, Anopheles arabiensis, in two insectaries (Ifakara, Tanzania and Glasgow, UK). The heads and thoraces of female mosquitoes of two age classes (1-9 day-olds and 10-17 day-olds) were scanned using an attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectrometer (4000 cm-1 to 400 cm-1). The dimensionality of the spectra data was reduced using unsupervised principal component analysis (PCA) or t-distributed stochastic neighbour embedding (t-SNE), and then the spectra were used to train deep learning (DL) and standard machine learning (ML) classifiers. Transfer learning was also evaluated for improving the computational cost of the models when predicting mosquito age classes from new populations. Model accuracies for predicting the age of test mosquitoes from the same insectary as the training samples reached 99% for DL and 92% for ML, but did not generalise to a different insectary, achieving only 46% and 48% for ML for DL, respectively. Dimensionality reduction did not improve the model generalisability between locations but reduced computational time up to 5-fold. However, transfer learning by updating pre-trained models with 2% of mosquitoes from the alternate location brought both DL and standard ML model performance to ~98% accuracy for predicting mosquito age classes in the alternative insectary. Combining dimensionality reduction and transfer learning can reduce computational costs and improve the transferability of both deep learning and standard machine learning models for predicting the age of mosquitoes. Future studies could investigate the optimal quantities and diversity of training data necessary for transfer learning, and implications for broader generalisability to unseen datasets.

bioinformatics↗

bioRxiv2020

Rapid ageing and species identification of natural mosquitoes for malaria surveillance

The malaria parasite, which is transmitted by several Anopheles mosquito species, requires more time to reach its human-transmissible stage than the average lifespan of a mosquito. Monitoring the species-specific age structure of mosquito populations is critical to evaluating the impact of vector control interventions on malaria risk. We developed a rapid, cost-effective surveillance method based on deep learning of mid-infrared spectra of mosquitoes cuticle that simultaneously identifies the species and the age of three main malaria vectors, in natural populations. Using over 40,000 ecologically and genetically diverse females, we could speciate and age grade An. gambiae, An. arabiensis, and An. coluzzii with up to 95% accuracy. Further, our model learned the age of new populations with minimal sampling effort and detected the impact of control interventions on simulated mosquito populations, measured as a shift in their age structures. We anticipate our method to be applied to other arthropod vector-borne diseases.

zoology↗